

import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowercase			:				List[str]   =     False
lowercase			:				str   =     True
lowercase			:				Optional[Any]   =     False


if __name__ == "__main__":
		lowercase			:				List[str]   =     argparse.ArgumentParser()

		parser.add_argument(
		    "--repo_path",
		    default=None,
		    type=str,
		    required=True,
		    help="The config json file corresponding to the architecture.",
		)

		parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

		lowercase			:				List[str]   =     parser.parse_args()

		lowercase			:				Optional[Any]   =     {
		    "image_size": "sample_size",
		    "num_res_blocks": "layers_per_block",
		    "block_channels": "block_out_channels",
		    "down_blocks": "down_block_types",
		    "up_blocks": "up_block_types",
		    "downscale_freq_shift": "freq_shift",
		    "resnet_num_groups": "norm_num_groups",
		    "resnet_act_fn": "act_fn",
		    "resnet_eps": "norm_eps",
		    "num_head_channels": "attention_head_dim",
		}

		lowercase			:				Optional[int]   =     {
		    "time_steps": "time_proj",
		    "mid": "mid_block",
		    "downsample_blocks": "down_blocks",
		    "upsample_blocks": "up_blocks",
		}

		lowercase			:				Dict   =     "" if has_file(args.repo_path, "config.json") else "unet"

		with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
				lowercase			:				Any   =     reader.read()
				lowercase			:				List[str]   =     json.loads(text)

		if do_only_config:
				for key in config_parameters_to_change.keys():
						config.pop(key, None)

		if has_file(args.repo_path, "config.json"):
				lowercase			:				Dict   =     UNetaDModel(**config)
		else:
				lowercase			:				List[Any]   =     UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
				lowercase			:				int   =     class_name(**config)

		if do_only_config:
				model.save_config(os.path.join(args.repo_path, subfolder))

		lowercase			:				Union[str, Any]   =     dict(model.config)

		if do_only_renaming:
				for key, value in config_parameters_to_change.items():
						if key in config:
								lowercase			:				str   =     config[key]
								del config[key]

				lowercase			:				List[Any]   =     [k.replace("UNetRes", "") for k in config["down_block_types"]]
				lowercase			:				str   =     [k.replace("UNetRes", "") for k in config["up_block_types"]]

		if do_only_weights:
				lowercase			:				Union[str, Any]   =     torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

				lowercase			:				Optional[Any]   =     {}
				for param_key, param_value in state_dict.items():
						if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
								continue
						lowercase			:				Optional[int]   =     False
						for key, new_key in key_parameters_to_change.items():
								if not has_changed and param_key.split(".")[0] == key:
										lowercase			:				Any   =     param_value
										lowercase			:				Union[str, Any]   =     True
						if not has_changed:
								lowercase			:				Optional[int]   =     param_value

				model.load_state_dict(new_state_dict)
				model.save_pretrained(os.path.join(args.repo_path, subfolder))









import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


lowercase			:				Optional[int]   =     logging.get_logger(__name__)

lowercase			:				Optional[Any]   =     {"vocab_file": "vocab.txt"}

lowercase			:				Optional[int]   =     {
    "vocab_file": {
        "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt",
        "YituTech/conv-bert-medium-small": (
            "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt"
        ),
        "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt",
    }
}

lowercase			:				Tuple   =     {
    "YituTech/conv-bert-base": 5_12,
    "YituTech/conv-bert-medium-small": 5_12,
    "YituTech/conv-bert-small": 5_12,
}


lowercase			:				Optional[int]   =     {
    "YituTech/conv-bert-base": {"do_lower_case": True},
    "YituTech/conv-bert-medium-small": {"do_lower_case": True},
    "YituTech/conv-bert-small": {"do_lower_case": True},
}







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		int								=	VOCAB_FILES_NAMES
							UpperCAmelCase_						:		Optional[int]								=	PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase_						:		List[Any]								=	PRETRAINED_INIT_CONFIGURATION
							UpperCAmelCase_						:		Optional[Any]								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase_						:		Optional[int]								=	ConvBertTokenizer




							def __init__(					self    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase="[UNK]"    ,						__UpperCAmelCase="[SEP]"    ,						__UpperCAmelCase="[PAD]"    ,						__UpperCAmelCase="[CLS]"    ,						__UpperCAmelCase="[MASK]"    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase=None    ,						**__UpperCAmelCase    ,						)		->       Optional[int]:
														super().__init__(
														    __UpperCAmelCase    ,						tokenizer_file=__UpperCAmelCase    ,						do_lower_case=__UpperCAmelCase    ,						unk_token=__UpperCAmelCase    ,						sep_token=__UpperCAmelCase    ,						pad_token=__UpperCAmelCase    ,						cls_token=__UpperCAmelCase    ,						mask_token=__UpperCAmelCase    ,						tokenize_chinese_chars=__UpperCAmelCase    ,						strip_accents=__UpperCAmelCase    ,						**__UpperCAmelCase    ,						)

														A      :					Any      		=					json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
														if (
														    normalizer_state.get('''lowercase'''    ,						__UpperCAmelCase						) != do_lower_case
														    or normalizer_state.get('''strip_accents'''    ,						__UpperCAmelCase						) != strip_accents
														    or normalizer_state.get('''handle_chinese_chars'''    ,						__UpperCAmelCase						) != tokenize_chinese_chars
														):
																					A      :					Tuple      		=					getattr(__UpperCAmelCase    ,						normalizer_state.pop('''type'''						)						)
																					A      :					int      		=					do_lower_case
																					A      :					int      		=					strip_accents
																					A      :					Union[str, Any]      		=					tokenize_chinese_chars
																					A      :					str      		=					normalizer_class(**__UpperCAmelCase						)

														A      :					str      		=					do_lower_case




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=None						)		->       Optional[int]:
														A      :					Dict      		=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

														if token_ids_a:
																					output += token_ids_a + [self.sep_token_id]

														return output




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None						)		->       List[int]:
														A      :					List[Any]      		=					[self.sep_token_id]
														A      :					Optional[Any]      		=					[self.cls_token_id]
														if token_ids_a is None:
																					return len(cls + token_ids_a + sep						) * [0]
														return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None						)		->       Tuple[str]:
														A      :					Optional[int]      		=					self._tokenizer.model.save(__UpperCAmelCase    ,						name=__UpperCAmelCase						)
														return tuple(__UpperCAmelCase						)






